Prognostic Value of Computed Tomography and/or 18F-Fluorodeoxyglucose Positron Emission Tomography Radiomics Features in Locally Advanced Non-small Cell Lung Cancer

Angel Moran, Yichuan Wang, Brandon A. Dyer, Stephen S.F. Yip, Megan E. Daly, Tokihiro Yamamoto

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Introduction: We investigated whether adding computed tomography (CT) and/or 18F-fluorodeoxyglucose (18F-FDG) PET radiomics features to conventional prognostic factors (CPFs) improves prognostic value in locally advanced non-small cell lung cancer (NSCLC). Materials and Methods: We retrospectively identified 39 cases with stage III NSCLC who received chemoradiotherapy and underwent planning CT and staging 18F-FDG PET scans. Seven CPFs were recorded. Feature selection was performed on 48 CT and 49 PET extracted radiomics features. A penalized multivariate Cox proportional hazards model was used to generate models for overall survival based on CPFs alone, CPFs with CT features, CPFs with PET features, and CPFs with CT and PET features. Linear predictors generated and categorized into 2 risk groups for which Kaplan-Meier survival curves were calculated. A log-rank test was performed to quantify the discrimination between the groups and calculated the Harrell's C-index to quantify the discriminatory power. A likelihood ratio test was performed to determine whether adding CT and/or PET features to CPFs improved model performance. Results: All 4 models significantly discriminated between the 2 risk groups. The discriminatory power was significantly increased when CPFs were combined with PET features (C-index 0.82; likelihood ratio test P < .01) or with both CT and PET features (0.83; P < .01) compared with CPFs alone (0.68). There was no significant improvement when CPFs were combined with CT features (0.68). Conclusion: Adding PET radiomics features to CPFs yielded a significant improvement in the prognostic value in locally advanced NSCLC; adding CT features did not.

Original languageEnglish (US)
Pages (from-to)461-468
Number of pages8
JournalClinical lung cancer
Volume22
Issue number5
DOIs
StatePublished - Sep 2021

Keywords

  • Carcinoma
  • Chemoradiotherapy
  • Imaging
  • Survival analysis
  • Texture features

ASJC Scopus subject areas

  • Oncology
  • Pulmonary and Respiratory Medicine
  • Cancer Research

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